Adaptive large neighborhood search algorithm – performance evaluation under parallel schemes & applications

Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers

트럭을 이동형 드론 기지로 사용하는 한정용량 트럭-드론 경로 배정 문제

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Drones initially received attention for military purposes as a collective term for unmanned aerial vehicles (UAVs), but recently, efforts to use them in logistics have been actively underway. If drones are put into places where low-weight and high-value items are currently difficult to deliver by existing delivery means, it will have the effect of greatly reducing costs. However, the disadvantages of drones in delivery are also clear. In order to improve the delivery capacity of drones, the size of drones must increase when drones are equipped with large-capacity batteries. This thesis introduced two methods and presented algorithms for each method among VRP-D. First of all, CVP-D is a method in which carriers such as trucks and ships with large capacity and slow speed carry robots and drones with small capacity. Next, in the CVRP-D, the vehicle and the drone move different paths simultaneously, and the drone can visit multiple nodes during one sortie. The two problems are problems in which restrictions are added to the vehicle route problem (VRP), known as the NP-hard problem. The algorithm presented in this study derived drone-truck routes for two problems within a reasonable time. In addition, sensitivity analysis was conducted to observe changes in the appropriate network structure for the introduction of drone delivery and the main parameters of the drone. In addition, the validity of the proposed algorithm was verified through comparison with the data used as a benchmark in previous studies. These research results will contribute to the creation of delivery routes quickly, considering the specification of a drone.드론은 무인항공기(UAV)의 통칭으로 초기에는 군사적 목적으로 주목을 받았으나 최근 물류에서 사용하려는 노력이 적극적으로 진행되고 있다. 드론이 저중량-고가치 물품을 배송에서 현재 기존 배송수단에 의해 배송이 어려운 곳에 투입이 된다면 큰 비용절감의 효과가 있을 것이다. 하지만 배송에 있어서 드론의 단점도 명확하다. 드론의 배송능력을 향상시키기 위해서는 드론이 대용량 배터리를 탑재하면 드론 크기가 증가하여야 한다. 이러한 단점을 극복하기 위해서 드론과 트럭을 결합하여 운영하는 방식이 연구되어왔다. 이러한 방식 중 본 연구에서는 두 가지 방식을 소개하고, 각각의 방식에 대한 알고리즘을 제시하였다. 먼저, CVP-D는 용량이 크고 속도가 느린 트럭이나 배 등의 캐리어가 용량이 작은 로봇, 드론 등을 싣고 다니면서 배송을 하는 방식이다. 다음으로, CVRP-D는 차량과 드론이 동시에 각기 다른 경로를 이동하며, 드론은 1회 비행(sortie)시 다수의 노드를 방문하는 것이 가능하다. 두 문제는 차량경로문제(VRP)에 제약이 더해진 문제이다. VRP는 대표적인 NP-hard 문제로 해를 구하기 위해서 휴리스틱 알고리즘이 요구된다. 본 연구에서 제시하는 알고리즘은 합리적인 시간 내 두문제의 드론-트럭 경로를 도출하였다. 또한 민감도 분석을 실시하여 드론 배송 도입을 위한 적절한 네트워크 구조 및 드론의 주요 파라미터에 변화에 따른 변화를 관찰하였다. 이는 차후 드론의 성능에 관한 의사결정 시 고려해야 할 요소들에 대한 기준이 될 수 있을 것으로 기대된다. 또한 선행연구에서 벤치마크로 사용되는 데이터와의 비교를 통해 제안하는 알고리즘의 타당성을 검증하였다. 본 연구는 드론 도입이 배송시간을 감소시키며, 운영방법에 따라서 배송시간의 차이가 발생함을 보였다. 이러한 연구 성과는 드론 배송 시 환경과 기계적 성능을 고려한 배송 경로를 단시간내 생성하여 상업적으로 이용가능 할 것이다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Purpose 3 1.3 Contribution of Research 4 Chapter 2. Literature review 5 2.1 Vehicle Routing Problems with Drone 5 2.2 Carrier Vehicle Problem with Drone(CVP-D) 10 2.3 Capacitated VRP with Drone(CVRP-D) 12 Chapter 3. Mathematical Formulation 14 3.1 Terminology 14 3.2 CVP-D Formulation 15 3.3 CVRP-D Formulation 19 Chapter 4. Proposed Algorithms 23 4.1 Heuristic Algorithm 23 4.1.1 Knapsack Problem 23 4.1.2 Parallel Machine Scheduling (PMS) 25 4.1.3 Set Covering Location Problem (SCLP) 27 4.1.4 Guided Local Search (GLS) Algorithm 28 4.1.5 Genetic Algorithm (GA) 29 4.2 Proposed Heuristic Algorithm : GA-CVPD 30 4.3 Proposed Heuristic Algorithm : GA-CVRPD 33 Chapter 5. Numerical Analysis 36 5.1 Data Description 36 5.2 Numerical experiment 37 5.3 Sensitivity analysis 39 5.3.1 Analysis on GA-CVPD 39 5.3.2 Analysis on GA-CVRPD 42 5.3.3 Result on different Instances 45 Chapter 6. Conclusion 48 Bibliography 50 Abstract in Korean 53 4.1.5 Genetic Algorithm (GA) 29 4.2 Proposed Heuristic Algorithm : GA-CVPD 30 4.3 Proposed Heuristic Algorithm : GA-CVRPD 33 Chapter 5. Numerical Analysis 36 5.1 Data Description 36 5.2 Numerical experiment 37 5.3 Sensitivity analysis 42 5.3.1 Analysis on GA-CVPD 39 5.3.2 Analysis on GA-CVRPD 42 5.3.3 Result on different Instances 45 Chapter 6. Conclusion 48 Bibliography 50 Abstract in Korean 53석

Arc routing problems: A review of the past, present, and future

[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emerging applicationsThis research was supported by the Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number: PGC2018-099428-B-I00. The Research Council of Norway, Grant/Award Numbers: 246825/O70 (DynamITe), 263031/O70 (AXIOM).Corberán, Á.; Eglese, R.; Hasle, G.; Plana, I.; Sanchís Llopis, JM. (2021). Arc routing problems: A review of the past, present, and future. Networks. 77(1):88-115. https://doi.org/10.1002/net.21965S8811577

Drone-aided routing:A literature review

The interest in using drones in various applications has grown significantly in recent years. The reasons are related to the continuous advances in technology, especially the advent of fast microprocessors, which support intelligent autonomous control of several systems. Photography, construction, and monitoring and surveillance are only some of the areas in which the use of drones is becoming common. Among these, last-mile delivery is one of the most promising areas. In this work we focus on routing problems with drones, mostly in the context of parcel delivery. We survey and classify the existing works and we provide perspectives for future research.</p

Hybrid Vehicle-drone Routing Problem For Pick-up And Delivery Services Mathematical Formulation And Solution Methodology

The fast growth of online retail and associated increasing demand for same-day delivery have pushed online retail and delivery companies to develop new paradigms to provide faster, cheaper, and greener delivery services. Considering drones’ recent technological advancements over the past decade, they are increasingly ready to replace conventional truck-based delivery services, especially for the last mile of the trip. Drones have significantly improved in terms of their travel ranges, load-carrying capacity, positioning accuracy, durability, and battery charging rates. Substituting delivery vehicles with drones could result in $50M of annual cost savings for major U.S. service providers. The first objective of this research is to develop a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. Anticipating the potential risk of using drones for delivery services, aviation authorities in the U.S. and abroad have mandated necessary regulatory rules to ensure safe operations. The U.S. Federal Aviation Administration (FAA) is examining the feasibility of drone flights in restricted airspace for product delivery, requiring drones to fly at or below 400-feet and to stay within the pilot’s line of sight (LS). Therefore, a second objective of this research is considered to develop a modeling framework for the integrated vehicle-drone routing problem for pick-up and delivery services considering the regulatory rule requiring all drone flights to stay within the pilot’s line of sight (LS). A mixed integer program (MIP) and an efficient solution methodology were developed for the problem. The solution determines the optimal vehicle and drone routes to serve all customers without violating the LS rule such that the total routing cost of the integrated system is minimized. Two different heuristics are developed to solve the problem, which extends the Clarke and Wright Algorithm to cover the multimodality aspects of the problem and to satisfy the LS rule. The first heuristic implements a comprehensive multimodal cost saving search to construct the most efficient integrated vehicle-drone routes. The second heuristic is a light version of the first heuristic as it adopts a vehicle-driven cost saving search. Several experiments are conducted to examine the performance of the developed methodologies using hypothetical grid networks of different sizes. The capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system is illustrated. In addition, a case study is presented in which the developed methodology is applied to provide pick-up and delivery services in the downtown area of the City of Dallas. The results show that mandating the LS rule could double the overall system operation cost especially in dense urban areas with LS obstructions